Billions of bushels of grain (corn, soybeans, wheat, sorghum) are grown each year in the United States and enter a distribution network for purchase and utilization by various consumers, including animal feeds, industrial uses and human food processing. Grain varies in its quality and physical attributes from location to location due to genetic differences, local environmental conditions, agronomic production practices and physical handling and shipping treatment. Grains from different locations are combined in large storage and shipping containers for both domestic and export use.
In order to protect the consumer and provide assurances that the product purchased meets consumer needs, the U.S. Congress enacted the United States Grain Standards Act (USGSA) (Aug. 11, 1916, ch. 313, 39 Stat. 453 (7 USC .sctn. 71 to 87, 111, 113, 241 to 273, 2209; 16 USC .sctn..sctn. 490, 683) in 1916 to provide a uniform descriptive system for long distance trading of grain. The Federal Grain Inspection Service (FGIS) was created within the U.S. Department of Agriculture (USDA) to: (1) establish uniform Grades and Standards, and (2) to implement nationwide procedures for accurate and unbiased test results. In general, the Grain Standards factors assess physical condition or biological stability of the grain and generally fall into at least one of the following categories:                Grade determining: provides a numerical Grade based on the level of the poorest of factors including test weight, heat damage, total damage, broken corn/foreign material (BCFM).        Mandatory non-grade determining: grain moisture, broken corn, and foreign material.        Class: grain color or type—yellow, white, mixed.        Special Grade designations: special situations, i.e., insect infestation, type of grain endosperm, i.e., waxy, flint.        Optional official criteria: factors requested by party requesting inspection, i.e., protein percent in wheat, which is a measure of end-use value.(See, “Quality Corn; The United States Grades and Standards”; Iowa Corn Growers Association, January, 1990, No. 2 of 6.)        
Since the inherent quality of grains is not routinely measured and included in the USGSA, end-users have set their own internal standards to assure the grain purchased for their processing needs provides the greatest efficiency and return. Such factors may include, but are not limited to, color gradation, percent protein, oil, starch and hard endosperm. These traits are of particular interest to wet and dry millers of corn.
Wet millers process grain by steeping the grain in liquid(s) of varying composition to extract the starch, protein, gluten, oil, and hulls. Starch is further processed for various industrial starch uses, or converted into sweeteners or alcohol. Protein and gluten fractions are generally sold as animal feeds.
Dry millers process grain by mechanically breaking and separating the grain fractions through a series of rollers and shakers into the various sized components. Based on size and composition, these components are referred to as grits, meal, flour, germ and bran. These fractions are purchased by end-users for processing into cereals, snack foods, baking products, brewing and other industrial uses. Larger grit particle size is of the greatest value. A variant of dry milling is alkaline-cooking which produces dry masa flour for Mexican foods such as tortillas and snack foods.
In recent years, the ability to identify, preserve, ship and distribute grains with specific traits of added value to the seedsman, the grower, and the end-user, has increased interest in a systematic means of consistently characterizing grain for those specific traits of interest. Further, plant breeders need quick, accurate, reliable analysis methods on which to base individual plant selections in their breeding schemes. The specific traits of particular interest to millers vary from mill to mill depending upon the milling process and the type of product manufactured by the end-user. In general, for wet milling, if grain is US Grade No. 2 or 3, no other additional requirements exist for acceptance. There are some exceptions, such as wet mills that require highly extractable starch, or starch with unique cooking attributes.
In dry milling and alkaline cooking, where food products are manufactured, a host of grain characteristics may be evaluated to determine which hybrids produce grain which best meet the specific needs. In general, color gradation and hardness are two grain characteristics of particular interest because of consumer preference in the appearance and texture of the food. Grain color has a direct impact on product color, especially in alkaline cooking. Hardness affects ease of milling as well as product texture and yield, and is essentially a function of the relative proportion of vitreous (hard) to floury (soft) endosperm in the grain. Some grain cooking characteristics, such as pericarp removal, are also critical in the manufacture of food products, but these traits cannot be evaluated without expensive milling/cooking trials (actual or simulated), and therefore are not measured by millers until the final stages of hybrid selection.
White corn processors prefer a “clean” white color, without tones of yellow, red or a “dirty” (i.e., gray) cast. Yellow food corn processors prefer a “bright, medium-yellow” color. As the descriptions suggest, color ratings are highly subjective. Some analyses attempt to compare grain to a standard color chart, e.g., Hunter Color Scale, to evaluate grain (see, “Intrinsic Value of Nebraska Corn: 1994 Crop Year Report”; Jackson, Nebr. Corn Board, P.O. Box 95107, 301 Centennial Mall South, Lincoln, Nebr. 68509-5107); but more often ratings over a scale of 1-5 are given based on the expertise of the observer. Ratings of 4-5 are too dark, and 1-2 may be too light or pale in color. Grain with unacceptable color results in products that have unsatisfactory consumer acceptance. To date, the color analysis of seeds and grain for milling purposes is still commonly based upon subjective ratings.
Quantitative characteristics of grain seed including protein, oil and starch content as well as methods of qualitatively distinguishing between seed types have been available for some time. Some examples of such methods are described in U.S. Pat. No. 3,385,434 to Nelson, U.S. Pat. No. 3,830,289 to Gray, U.S. Pat. No. 4,260,262 to Webster and U.S. Pat. No. 4,734,584 to Rosenthal.
Nelson teaches a method of sorting seed corn from field corn based on the transluminescent characteristics of each. The apparatus described by Nelson contains a strong light beamed from multiple directions against the kernels of corn in a manner that allows for detection and comparison of reflected and transluminescent light. Nelson then sorts the kernels according to their transluminescent characteristics while ignoring the surface reflected light.
Gray teaches that Nelson's method is relatively unreliable in practice because of the unpredicted effects of reflected radiation and because of the size difference of the seed corn kernels. Gray provides a sorting method based on measuring and comparing the shadow pattern of at least two areas of light attenuation through a seed. Neither Gray nor Nelson teach the existence of a correlation between hard endosperm percent and the amount of transmitted light or a manner of calculating same.
Webster describes the use of photo-optical grain quality analyzers that calculates seed characteristics, such as oil percentage, water percentage, and protein percentage, from measurement of reflected infrared light.
Rosenthal provides an apparatus for near infrared illumination of seeds and detection of reflected light from same for calculation of seed characteristics.
See also the Abstract of a presentation by M. R. Paulsen, (Machine Vision for Corn Inspection, Abstract of presentation at the 1992 Grain Quality Conference in Champaign, Ill., Mar. 17, 1992) wherein is disclosed a machine vision system containing an image processing board, a microcomputer with monitor, a display monitor, a solid state CCD camera, and a lighting chamber for holding samples. Paulsen describes the following four applications for his system which include measurement of kernel length, detection of stress cracks in kernels, detection of cracks in the pericarp with the use of dye staining and distinguishing between whole and broken kernels. Paulsen states that development of his system is continuing in an effort to detect corn color and kernel hardness. The Abstract does not include a detailed description of how to repeat Paulsen's work or detailed results of the accuracy or reliability of the methods employed.
One of skill in the art will appreciate that a corn kernel consists of a germ (embryo) and endosperm covered by a seed coat or pericarp. The germ is the major oil source within the kernels and accounts for about 11% of the kernel. Approximately 83% of the kernel consists of endosperm with a composition mostly of starch but also protein and other constituents. The pericarp (bran) accounts for another 5%, and the tip cap constitutes the remaining 1% of the kernel. The makeup of the endosperm determines the processing usage of the grain. Endosperm consists of varying percentages of soft and hard endosperm. Generally, soft endosperm grains are preferred by wet millers and hard endosperm grains are preferred in dry milling and alkaline cooking. Starch is typically more easily extracted from soft endosperm grain types. Kernels with a high proportion of hard endosperm are less likely to break during shipping, will produce a high dry milling yield of large more valuable grit components, and in the alkaline cooking process are less likely to be overcooked or damaged. There is an optimum hardness for alkaline cooking, however, and hybrids with very hard (flinty) grain are undesirable because they require too long to cook.
Numerous ways of estimating grain hardness have been employed over the years. Hardness, as the ratio of hard/soft endosperm, has been quantified directly with time-consuming measurements on dissected kernels, and has also been estimated with subjective ratings. Another aspect of hardness, the resistance to crushing or breaking, has been approximated using various physical devices designed to simulate milling processes or grain handling stresses. Hardness has also been estimated in an indirect way by measuring grain density (weight per unit volume), which is a highly correlated trait. Measurements of both density and resistance to breakage are influenced by grain moisture content, and require either that all samples be of similar moisture or that a correction factor be used.
Visual observation of grain over a light box, followed by a subjective rating given by an experienced person, has long been a common screening method for grain hardness. Soft endosperm is more opaque and does not transmit much light, while hard endosperm is more translucent and transmits more light. Although an important benefit of this method is time efficiency, such ratings depend on the “eye of the beholder” and vary based on the person rating the grain, so that hybrids producing acceptable grain for one processor may not be acceptable to another. In a breeding program to improve grain texture of the plant's seed or of the final grain, high data variability often occurs due to multiple raters. To counteract this a single person bears the burden of observing and rating thousands of samples to minimize such variation. Even then, variation occurs due to fatigue and observer errors.
A convenient and commonly accepted method of estimating hardness in the grain industry is the measurement of test weight, a density-based assessment. Actually, test weight is not a measure of true density but bulk density, and is obtained by weighing a given volume of grain and making an adjustment for moisture. Test weight is widely used as a quick test for quality (and grade determination) of commodity grains at points of sale, in breeding yield trials, and even in the approval of grain for wet milling applications. Test weight has also been shown in some instances to be positively correlated with dry milling yield and is used by some food grade end-users. It is known, however, that test weight data can be misleading due to the confounding of other grain characteristics such as kernel shape, and therefore it's value is limited.
Another conventional and commonly performed test is the “floaters” test. Although this test purports to provide an indirect measure of kernel hardness, it is actually more a measure of the uniformity of the grain in regard to density. In this test, the number of kernels which float in a 1.275 specific gravity solution are counted, and relative hardness is read from a chart which accounts for grain moisture. Processors generally specify a maximum percent floaters allowable. This is a fairly time consuming process of counting a given number of kernels per sample, involving placing the kernels in the solution, counting and removing same from the solution.
Another commonly used device for evaluating density is the pycnometer, which depends upon the displacement of gas. A weighed grain sample of optimum moisture is placed in a sealed chamber followed by pressurized gas, such as nitrogen, which displaces the atmospheric air in the chamber. The amount of gas entering the chamber is related to hardness, and is a function of grain porosity, endosperm texture, as well as inter-seed space.
Scientists and end-users have also attempted to quantify hardness based upon resistance to physical damage. A hardness index developed using a tangential abrasive dehulling device (TADD) was used in a 1995 Texas Foodcorn Performance Test, as described by Bockholt et al. (1995 Texas Food Corn Performance Test; The Texas Agricultural Experiment, Station/Department of Soil & Crop Sciences, Texas A&M University, College Station, Texas). In that test, the amount of material removed, expressed as a percentage of the total sample, is related to the relative proportions of hard to soft endosperm, kernel size and shape, and type of denting. It requires 45 gm of whole kernels and is a destructive technique. Seeds cannot be later used for any purpose, which is a major drawback in a breeding program, where it is desirable to select and plant the best kernels.
Near-infrared reflectance (NIR) and near-infrared transmittance (NIT) analyzers are becoming increasingly popular for quick estimation of numerous grain traits including moisture, extractable starch, test weight, and kernel density. Grain samples are irradiated with light of varying wavelengths over a range from about 400 to 2,500 nm. Electronic detectors measure the absorbance of light by the grain at the different wavelengths. The pattern of absorption is a function of the type and concentration of chemical bonds in molecules within the grain. Statistical programs are available to solve the complicated calibrations which link the absorption patterns to actual grain properties measured with standard procedures. NIR and NIT analyzers offer some advantages because they are rapid and non-destructive. The reliability of the results depends upon the quality of the calibrations, and precision can only approach but never equal that of measurements obtained directly with actual procedures.
More recently, a method and apparatus disclosed in U.S. Pat. No. 5,835,206 to Tragesser, the entire disclosure of which is hereby incorporated herein by reference, describes the use of a color image analyzer for quantifying seed/grain quality traits. The main benefit of Tragesser's disclosed method and apparatus is a fairly quick and subjective estimate of grain hardness, through the calculation of hard endosperm area as a percent of total kernel area. With Tragesser's apparatus, a color video camera and a computer are used to capture and analyze white light that is passed through a back-lit seed/grain sample. FIG. 1 shows an example of Tragesser's apparatus. As illustrated, light is directed through a first linear polarizing light filter (25) and then through the seed sample (10). Light transmitted through the seed sample is collected by a video camera (15) after having passed through a second linear polarizing light filter (7) that is oriented with its direction of polarization orthogonal to that of the first polarizing filter (25). The use of cross polarizing filters in this manner prevents extraneous background/ambient light that does not pass through the seed/grain sample from interfering with the analysis of light passing through the sample. Pixel intensity and color data are acquired from the camera and analyzed, using conventional software, to quantify certain grain traits such as hard endosperm percent, kernel area, hue (gradation of color), saturation (whiteness), and intensity. Since the quantification of grain hue and saturation is based on whole kernel images, it is influenced by embryo color and size, as well as grain texture. For certain end-users, the estimations of grain hue and saturation using this method can be occasionally inappropriate because unused portions of the kernel (embryo, soft endosperm) can influence the results.
There remains a need for a convenient and accurate method and apparatus for evaluating important quality indicative characteristics of seeds/grains such as, for example, grain color. There remains a need for a method and apparatus employing non-polarized visible light. There furthermore remains a need for a method and apparatus that does not employ near-infrared reflectance (NIR) or near-infrared transmittance (NIT) analyzers, or external polarizers but instead uses direct visual range light.